Ribosome-induced changes in elongation factor Tu conformation control GTP hydrolysis.

In translation, elongation factor Tu (EF-Tu) molecules deliver aminoacyl-tRNAs to the mRNA-programmed ribosome. The GTPase activity of EF-Tu is triggered by ribosome-induced conformational changes of the factor that play a pivotal role in the selection of the cognate aminoacyl-tRNAs. We present a 6.7-A cryo-electron microscopy map of the aminoacyl-tRNA x EF-Tu x GDP x kirromycin-bound Escherichia coli ribosome, together with an atomic model of the complex obtained through molecular dynamics flexible fitting. The model reveals the conformational changes in the conserved GTPase switch regions of EF-Tu that trigger hydrolysis of GTP, along with key interactions, including those between the sarcin-ricin loop and the P loop of EF-Tu, and between the effector loop of EF-Tu and a conserved region of the 16S rRNA. Our data suggest that GTP hydrolysis on EF-Tu is controlled through a hydrophobic gate mechanism.

Determination of signal-to-noise ratios and spectral SNRs in cryo-EM low-dose imaging of molecules.

Attempts to develop efficient classification approaches to the problem of heterogeneity in single-particle reconstruction of macromolecules require phantom data with realistic noise models. We have estimated the signal-to-noise ratios and spectral signal-to-noise ratios for three steps in the electron microscopic image formation from data obtained experimentally. An important result is that structural noise, i.e., the irreproducible component of the object prior to image formation, is substantial, and of the same order of magnitude as the reproducible signal. Based on this result, the noise modeling for testing new classification techniques can be improved.

Particle-verification for single-particle, reference-based reconstruction using multivariate data analysis and classification.

As collection of electron microscopy data for single-particle reconstruction becomes more efficient, due to electronic image capture, one of the principal limiting steps in a reconstruction remains particle-verification, which is especially costly in terms of user input. Recently, some algorithms have been developed to window particles automatically, but the resulting particle sets typically need to be verified manually. Here we describe a procedure to speed up verification of windowed particles using multivariate data analysis and classification. In this procedure, the particle set is subjected to multi-reference alignment before the verification. The aligned particles are first binned according to orientation and are binned further by K-means classification. Rather than selection of particles individually, an entire class of particles can be selected, with an option to remove outliers. Since particles in the same class present the same view, distinction between good and bad images becomes more straightforward. We have also developed a graphical interface, written in Python/Tkinter, to facilitate this implementation of particle-verification. For the demonstration of the particle-verification scheme presented here, electron micrographs of ribosomes are used.

Exploration of parameters in cryo-EM leading to an improved density map of the E. coli ribosome.

A number of image processing parameters in the 3D reconstruction of a ribosome complex from a cryo-EM data set were varied to test their effects on the final resolution. The parameters examined were pixel size, window size, and mode of Fourier amplitude enhancement at high spatial frequencies. In addition, the strategy of switching from large to small pixel size during angular refinement was explored. The relationship between resolution (in Fourier space) and the number of particles was observed to follow a lin-log dependence, a relationship that appears to hold for other data, as well. By optimizing the above parameters, and using a lin-log extrapolation to the full data set in the estimation of resolution from half-sets, we obtained a 3D map from 131,599 ribosome particles at 6.7A resolution (FSC=0.5).

SPIDER image processing for single-particle reconstruction of biological macromolecules from electron micrographs.

This protocol describes the reconstruction of biological molecules from the electron micrographs of single particles. Computation here is performed using the image-processing software SPIDER and can be managed using a graphical user interface, termed the SPIDER Reconstruction Engine. Two approaches are described to obtain an initial reconstruction: random-conical tilt and common lines. Once an existing model is available, reference-based alignment can be used, a procedure that can be iterated. Also described is supervised classification, a method to look for homogeneous subsets when multiple known conformations of the molecule may coexist.

SPIRE: the SPIDER reconstruction engine.

SPIRE is a Python program written to modernize the user interaction with SPIDER, the image processing system for electron microscopical reconstruction projects. SPIRE provides a graphical user interface (GUI) to SPIDER for executing batch files of SPIDER commands. It also lets users quickly view the status of a project by showing the last batch files that were run, as well as the data files that were generated. SPIRE handles the flexibility of the SPIDER programming environment through configuration files: XML-tagged documents that describe the batch files, directory trees, and presentation of the GUI for a given type of reconstruction project. It also provides the capability to connect to a laboratory database, for downloading parameters required by batch files at the start of a project, and uploading reconstruction results at the end of a project.

Particle picking by segmentation: a comparative study with SPIDER-based manual particle picking.

Boxing hundreds of thousands of particles in low-dose electron micrographs is one of the major bottle-necks in advancing toward achieving atomic resolution reconstructions of biological macromolecules. We have shown that a combination of pre-processing operations and segmentation can be used as an effective, automatic tool for identifying and boxing single-particle images. This paper provides a brief description of how this method has been applied to a large data set of micrographs of ice-embedded ribosomes, including a comparative analysis of the efficiency of the method. Some results on processing micrographs of tripeptidyl peptidase II particles are also shown. In both cases, we have achieved our goal of selecting at least 80% of the particles that an expert would select with less than 10% false positives.